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You could estimate the covariance matrices using trial and error. If not outrageously wrong, the covariances will converge eventually if my memory serves me right. Another method would be to apply the Autocovariance Least-Squares (ALS) technique (or possible other techniques) to estimate the noise covariances directly from sampled data. A quick search resulted in this paper which talks about the technique: Paper Link